{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "from numpy.linalg import cholesky"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10)"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 构造数据点\n",
    "\n",
    "sampleNo = 50\n",
    "\n",
    "x1 = np.arange(-4,6,10.0 / sampleNo)\n",
    "y1 = (0.5 * x1 +4) + np.random.rand(sampleNo, 1).T[0]\n",
    "plt.plot(x1,y1,'+')\n",
    "\n",
    "\n",
    "sampleNo = 40\n",
    "\n",
    "mu2 = np.array([[-4, 5]])\n",
    "s2 = np.random.rand(sampleNo, 2) * 2 + mu2\n",
    "plt.plot(s2[:,0],s2[:,1],'o')\n",
    "\n",
    "mu3 = np.array([[0, 7]])\n",
    "s3 = np.random.rand(sampleNo, 2) * 3 + mu3\n",
    "plt.plot(s3[:,0],s3[:,1],'.')\n",
    "\n",
    "\n",
    "mu4 = np.array([[3, 2]])\n",
    "s4 = np.random.rand(sampleNo, 2) * 2 + mu4\n",
    "plt.plot(s4[:,0],s4[:,1],'s')\n",
    "\n",
    "\n",
    "plt.xlim(-8,10)\n",
    "plt.ylim(1,10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 如图所示的样本，利用聚类并不好将其很好的分开,下面采用一种类似图像分割中大水漫灌的方式进行聚类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "拼接样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.00000000e+00,  2.90367320e+00,  1.00000000e+00],\n",
       "       [-3.80000000e+00,  2.60171383e+00,  1.00000000e+00],\n",
       "       [-3.60000000e+00,  2.68117434e+00,  1.00000000e+00],\n",
       "       [-3.40000000e+00,  2.43216042e+00,  1.00000000e+00],\n",
       "       [-3.20000000e+00,  2.49570341e+00,  1.00000000e+00],\n",
       "       [-3.00000000e+00,  2.67767216e+00,  1.00000000e+00],\n",
       "       [-2.80000000e+00,  3.41141986e+00,  1.00000000e+00],\n",
       "       [-2.60000000e+00,  3.26570739e+00,  1.00000000e+00],\n",
       "       [-2.40000000e+00,  3.07866180e+00,  1.00000000e+00],\n",
       "       [-2.20000000e+00,  3.21547369e+00,  1.00000000e+00],\n",
       "       [-2.00000000e+00,  3.55871140e+00,  1.00000000e+00],\n",
       "       [-1.80000000e+00,  4.09592490e+00,  1.00000000e+00],\n",
       "       [-1.60000000e+00,  3.40228163e+00,  1.00000000e+00],\n",
       "       [-1.40000000e+00,  4.04372873e+00,  1.00000000e+00],\n",
       "       [-1.20000000e+00,  3.71103522e+00,  1.00000000e+00],\n",
       "       [-1.00000000e+00,  4.17779241e+00,  1.00000000e+00],\n",
       "       [-8.00000000e-01,  4.35570894e+00,  1.00000000e+00],\n",
       "       [-6.00000000e-01,  3.89593559e+00,  1.00000000e+00],\n",
       "       [-4.00000000e-01,  4.66484047e+00,  1.00000000e+00],\n",
       "       [-2.00000000e-01,  4.41965355e+00,  1.00000000e+00],\n",
       "       [ 3.55271368e-15,  4.72369445e+00,  1.00000000e+00],\n",
       "       [ 2.00000000e-01,  4.99122381e+00,  1.00000000e+00],\n",
       "       [ 4.00000000e-01,  4.48740519e+00,  1.00000000e+00],\n",
       "       [ 6.00000000e-01,  4.85836096e+00,  1.00000000e+00],\n",
       "       [ 8.00000000e-01,  5.27840602e+00,  1.00000000e+00],\n",
       "       [ 1.00000000e+00,  5.18017914e+00,  1.00000000e+00],\n",
       "       [ 1.20000000e+00,  4.87019240e+00,  1.00000000e+00],\n",
       "       [ 1.40000000e+00,  5.32626076e+00,  1.00000000e+00],\n",
       "       [ 1.60000000e+00,  5.71934610e+00,  1.00000000e+00],\n",
       "       [ 1.80000000e+00,  4.98153581e+00,  1.00000000e+00],\n",
       "       [ 2.00000000e+00,  5.69773111e+00,  1.00000000e+00],\n",
       "       [ 2.20000000e+00,  5.80827993e+00,  1.00000000e+00],\n",
       "       [ 2.40000000e+00,  5.66422292e+00,  1.00000000e+00],\n",
       "       [ 2.60000000e+00,  5.44302255e+00,  1.00000000e+00],\n",
       "       [ 2.80000000e+00,  6.18159719e+00,  1.00000000e+00],\n",
       "       [ 3.00000000e+00,  5.99668013e+00,  1.00000000e+00],\n",
       "       [ 3.20000000e+00,  5.79460313e+00,  1.00000000e+00],\n",
       "       [ 3.40000000e+00,  6.00611355e+00,  1.00000000e+00],\n",
       "       [ 3.60000000e+00,  6.35587478e+00,  1.00000000e+00],\n",
       "       [ 3.80000000e+00,  6.15070372e+00,  1.00000000e+00],\n",
       "       [ 4.00000000e+00,  6.17013440e+00,  1.00000000e+00],\n",
       "       [ 4.20000000e+00,  6.32850407e+00,  1.00000000e+00],\n",
       "       [ 4.40000000e+00,  6.75393020e+00,  1.00000000e+00],\n",
       "       [ 4.60000000e+00,  6.79380658e+00,  1.00000000e+00],\n",
       "       [ 4.80000000e+00,  6.86845382e+00,  1.00000000e+00],\n",
       "       [ 5.00000000e+00,  6.50299439e+00,  1.00000000e+00],\n",
       "       [ 5.20000000e+00,  7.36045129e+00,  1.00000000e+00],\n",
       "       [ 5.40000000e+00,  7.42003635e+00,  1.00000000e+00],\n",
       "       [ 5.60000000e+00,  7.58379113e+00,  1.00000000e+00],\n",
       "       [ 5.80000000e+00,  7.71213629e+00,  1.00000000e+00],\n",
       "       [-3.08394897e+00,  6.32610087e+00,  2.00000000e+00],\n",
       "       [-2.50164675e+00,  6.88543421e+00,  2.00000000e+00],\n",
       "       [-2.14592145e+00,  6.47220786e+00,  2.00000000e+00],\n",
       "       [-3.60377109e+00,  5.47074322e+00,  2.00000000e+00],\n",
       "       [-2.99756883e+00,  6.30498373e+00,  2.00000000e+00],\n",
       "       [-2.89652345e+00,  5.50676001e+00,  2.00000000e+00],\n",
       "       [-3.37520991e+00,  6.22948896e+00,  2.00000000e+00],\n",
       "       [-3.75429794e+00,  5.83876991e+00,  2.00000000e+00],\n",
       "       [-2.55298397e+00,  6.87992148e+00,  2.00000000e+00],\n",
       "       [-2.23955700e+00,  6.99920400e+00,  2.00000000e+00],\n",
       "       [-3.37957422e+00,  5.74491705e+00,  2.00000000e+00],\n",
       "       [-3.59051329e+00,  6.48483472e+00,  2.00000000e+00],\n",
       "       [-3.50336303e+00,  6.46178034e+00,  2.00000000e+00],\n",
       "       [-2.33243953e+00,  5.73542978e+00,  2.00000000e+00],\n",
       "       [-2.99844224e+00,  5.37777131e+00,  2.00000000e+00],\n",
       "       [-3.82634680e+00,  5.31407621e+00,  2.00000000e+00],\n",
       "       [-3.33558248e+00,  5.90511631e+00,  2.00000000e+00],\n",
       "       [-2.94590963e+00,  6.78055223e+00,  2.00000000e+00],\n",
       "       [-2.52451328e+00,  6.19136030e+00,  2.00000000e+00],\n",
       "       [-2.00288163e+00,  5.96238036e+00,  2.00000000e+00],\n",
       "       [-2.12765296e+00,  6.69893767e+00,  2.00000000e+00],\n",
       "       [-2.83443398e+00,  5.77865360e+00,  2.00000000e+00],\n",
       "       [-2.13659213e+00,  6.78482703e+00,  2.00000000e+00],\n",
       "       [-2.88639073e+00,  6.16090985e+00,  2.00000000e+00],\n",
       "       [-3.46858057e+00,  5.35810158e+00,  2.00000000e+00],\n",
       "       [-3.03805683e+00,  5.40933970e+00,  2.00000000e+00],\n",
       "       [-3.36476435e+00,  6.24105582e+00,  2.00000000e+00],\n",
       "       [-3.28652545e+00,  5.97661924e+00,  2.00000000e+00],\n",
       "       [-3.76290099e+00,  5.49256620e+00,  2.00000000e+00],\n",
       "       [-2.89237418e+00,  5.95577549e+00,  2.00000000e+00],\n",
       "       [-3.72269213e+00,  5.65709838e+00,  2.00000000e+00],\n",
       "       [-3.28648567e+00,  6.06972675e+00,  2.00000000e+00],\n",
       "       [-2.70920241e+00,  5.09883316e+00,  2.00000000e+00],\n",
       "       [-3.48360704e+00,  5.32643562e+00,  2.00000000e+00],\n",
       "       [-3.38235451e+00,  6.72085254e+00,  2.00000000e+00],\n",
       "       [-2.78668810e+00,  5.41426459e+00,  2.00000000e+00],\n",
       "       [-3.42194905e+00,  6.90548723e+00,  2.00000000e+00],\n",
       "       [-3.66500156e+00,  6.79423641e+00,  2.00000000e+00],\n",
       "       [-2.78862355e+00,  5.54077085e+00,  2.00000000e+00],\n",
       "       [-2.15329697e+00,  5.56644355e+00,  2.00000000e+00],\n",
       "       [ 2.93166975e+00,  9.80947616e+00,  3.00000000e+00],\n",
       "       [ 2.29825906e+00,  7.18553047e+00,  3.00000000e+00],\n",
       "       [ 1.83634622e+00,  8.59961875e+00,  3.00000000e+00],\n",
       "       [ 1.55607679e+00,  9.06093272e+00,  3.00000000e+00],\n",
       "       [ 2.14443730e+00,  9.29182110e+00,  3.00000000e+00],\n",
       "       [ 1.42457384e+00,  8.60604897e+00,  3.00000000e+00],\n",
       "       [ 8.31938570e-01,  7.46896063e+00,  3.00000000e+00],\n",
       "       [ 1.58765048e+00,  9.37979029e+00,  3.00000000e+00],\n",
       "       [ 1.74347181e+00,  7.99167253e+00,  3.00000000e+00],\n",
       "       [ 2.47102480e+00,  9.88085939e+00,  3.00000000e+00],\n",
       "       [ 1.34210409e-01,  7.42123147e+00,  3.00000000e+00],\n",
       "       [ 1.25618808e-01,  9.51168124e+00,  3.00000000e+00],\n",
       "       [ 2.70520020e+00,  7.73136362e+00,  3.00000000e+00],\n",
       "       [ 1.41200005e+00,  7.08063945e+00,  3.00000000e+00],\n",
       "       [ 6.62155243e-01,  7.70746541e+00,  3.00000000e+00],\n",
       "       [ 1.63798750e+00,  7.00590506e+00,  3.00000000e+00],\n",
       "       [ 1.77858820e+00,  7.25590219e+00,  3.00000000e+00],\n",
       "       [ 3.55388192e-01,  8.38828389e+00,  3.00000000e+00],\n",
       "       [ 1.68693522e+00,  9.38494843e+00,  3.00000000e+00],\n",
       "       [ 7.44940846e-01,  7.81863613e+00,  3.00000000e+00],\n",
       "       [ 1.87231695e+00,  9.75653636e+00,  3.00000000e+00],\n",
       "       [ 1.76117233e+00,  7.38795329e+00,  3.00000000e+00],\n",
       "       [ 1.52529478e+00,  7.07469943e+00,  3.00000000e+00],\n",
       "       [ 2.12582262e+00,  9.23633156e+00,  3.00000000e+00],\n",
       "       [ 8.94516717e-01,  8.78197474e+00,  3.00000000e+00],\n",
       "       [ 9.42596998e-01,  8.49767252e+00,  3.00000000e+00],\n",
       "       [ 6.02758756e-01,  7.92403560e+00,  3.00000000e+00],\n",
       "       [ 1.00617964e+00,  7.25861841e+00,  3.00000000e+00],\n",
       "       [ 2.39409791e+00,  9.75511173e+00,  3.00000000e+00],\n",
       "       [ 2.61490990e+00,  8.29521866e+00,  3.00000000e+00],\n",
       "       [ 2.53080485e+00,  9.30434675e+00,  3.00000000e+00],\n",
       "       [ 5.77212718e-01,  8.46664855e+00,  3.00000000e+00],\n",
       "       [ 1.58871021e+00,  7.71365866e+00,  3.00000000e+00],\n",
       "       [ 2.23750806e+00,  8.04691196e+00,  3.00000000e+00],\n",
       "       [ 9.29751274e-01,  7.67856732e+00,  3.00000000e+00],\n",
       "       [ 2.67639111e+00,  8.43790188e+00,  3.00000000e+00],\n",
       "       [ 2.74892360e+00,  7.08090139e+00,  3.00000000e+00],\n",
       "       [ 1.37295088e+00,  8.02179585e+00,  3.00000000e+00],\n",
       "       [ 9.34119038e-01,  8.06726804e+00,  3.00000000e+00],\n",
       "       [ 7.34652771e-01,  7.16414348e+00,  3.00000000e+00],\n",
       "       [ 4.34961421e+00,  3.92404197e+00,  4.00000000e+00],\n",
       "       [ 4.71637627e+00,  2.94104408e+00,  4.00000000e+00],\n",
       "       [ 3.55400475e+00,  2.19551896e+00,  4.00000000e+00],\n",
       "       [ 4.92233411e+00,  2.36613639e+00,  4.00000000e+00],\n",
       "       [ 4.76532981e+00,  3.22336761e+00,  4.00000000e+00],\n",
       "       [ 3.67512098e+00,  3.99719132e+00,  4.00000000e+00],\n",
       "       [ 4.14817120e+00,  2.63289941e+00,  4.00000000e+00],\n",
       "       [ 4.62589269e+00,  2.90250794e+00,  4.00000000e+00],\n",
       "       [ 3.98434448e+00,  3.44688486e+00,  4.00000000e+00],\n",
       "       [ 4.02347798e+00,  3.79986394e+00,  4.00000000e+00],\n",
       "       [ 4.10843924e+00,  3.00418494e+00,  4.00000000e+00],\n",
       "       [ 3.32358630e+00,  2.45153685e+00,  4.00000000e+00],\n",
       "       [ 3.56825285e+00,  3.03002238e+00,  4.00000000e+00],\n",
       "       [ 3.80704077e+00,  3.86470243e+00,  4.00000000e+00],\n",
       "       [ 3.09861658e+00,  2.90824681e+00,  4.00000000e+00],\n",
       "       [ 4.00164919e+00,  3.06251087e+00,  4.00000000e+00],\n",
       "       [ 4.79464713e+00,  3.12443563e+00,  4.00000000e+00],\n",
       "       [ 4.41793833e+00,  3.99431329e+00,  4.00000000e+00],\n",
       "       [ 3.34243247e+00,  2.00725477e+00,  4.00000000e+00],\n",
       "       [ 3.94510431e+00,  2.36792862e+00,  4.00000000e+00],\n",
       "       [ 4.72135196e+00,  2.90892380e+00,  4.00000000e+00],\n",
       "       [ 3.06221742e+00,  2.08577742e+00,  4.00000000e+00],\n",
       "       [ 3.49648441e+00,  2.30034948e+00,  4.00000000e+00],\n",
       "       [ 4.70700246e+00,  2.82905058e+00,  4.00000000e+00],\n",
       "       [ 3.32270326e+00,  3.34936158e+00,  4.00000000e+00],\n",
       "       [ 3.16381915e+00,  3.64919177e+00,  4.00000000e+00],\n",
       "       [ 3.59294637e+00,  3.43779395e+00,  4.00000000e+00],\n",
       "       [ 3.36182480e+00,  2.06225603e+00,  4.00000000e+00],\n",
       "       [ 4.52958321e+00,  2.22779553e+00,  4.00000000e+00],\n",
       "       [ 4.09101077e+00,  2.10772441e+00,  4.00000000e+00],\n",
       "       [ 3.50609383e+00,  2.34012638e+00,  4.00000000e+00],\n",
       "       [ 4.49560776e+00,  2.38318656e+00,  4.00000000e+00],\n",
       "       [ 3.62323162e+00,  2.95826485e+00,  4.00000000e+00],\n",
       "       [ 3.40809027e+00,  2.45740618e+00,  4.00000000e+00],\n",
       "       [ 4.47293873e+00,  2.63630025e+00,  4.00000000e+00],\n",
       "       [ 3.63013991e+00,  2.67070956e+00,  4.00000000e+00],\n",
       "       [ 4.20200311e+00,  3.12689434e+00,  4.00000000e+00],\n",
       "       [ 3.67601518e+00,  3.42952623e+00,  4.00000000e+00],\n",
       "       [ 4.69276282e+00,  3.31672369e+00,  4.00000000e+00],\n",
       "       [ 4.49558195e+00,  3.72138989e+00,  4.00000000e+00]])"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = np.vstack((x1,y1,np.ones_like(x1))).T\n",
    "data2 = np.hstack((s2,2 * np.ones((len(s2),1))))\n",
    "data3 = np.hstack((s3,3 * np.ones((len(s3),1))))\n",
    "data4 = np.hstack((s4,4 * np.ones((len(s4),1))))\n",
    "data = np.vstack((data1,data2,data3,data4))\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先将全部样本的欧式距离计算并存储"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00000000e+03, 3.62187055e-01, 4.57717974e-01, ...,\n",
       "        7.69400614e+00, 8.70257066e+00, 8.53484466e+00],\n",
       "       [3.62187055e-01, 1.00000000e+03, 2.15206814e-01, ...,\n",
       "        7.52170701e+00, 8.52280818e+00, 8.37080369e+00],\n",
       "       [4.57717974e-01, 2.15206814e-01, 1.00000000e+03, ...,\n",
       "        7.31439864e+00, 8.31708110e+00, 8.16213793e+00],\n",
       "       ...,\n",
       "       [7.69400614e+00, 7.52170701e+00, 7.31439864e+00, ...,\n",
       "        1.00000000e+03, 1.02298591e+00, 8.69985113e-01],\n",
       "       [8.70257066e+00, 8.52280818e+00, 8.31708110e+00, ...,\n",
       "        1.02298591e+00, 1.00000000e+03, 4.50149998e-01],\n",
       "       [8.53484466e+00, 8.37080369e+00, 8.16213793e+00, ...,\n",
       "        8.69985113e-01, 4.50149998e-01, 1.00000000e+03]])"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data[:,:-1]\n",
    "y = data[:,-1]\n",
    "#     设置一个最大数，表示该点已经搜过过了\n",
    "MAX_DIST = 1000000\n",
    "dist = np.ones((len(X),len(X)),dtype='float64')*MAX_DIST\n",
    "for i in range(len(X)):\n",
    "    for j in range(len(X)):\n",
    "        if i != j :\n",
    "            dist[i][j] = (X[i][0] - X[j][0])**2 + (X[i][1] - X[j][1])**2 \n",
    "dist = np.sqrt(dist)\n",
    "dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用广度优先搜索，将与x1邻近的点找出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "def searchClass(data,dist,c,index):\n",
    "    # data为距离数据，dist为最大邻接距离，小于这个距离的两个点认为是同一类，c是点的颜色,搜索的索引\n",
    "    d = data.copy()\n",
    "#     随机选取一点\n",
    "    p = (index != 0).argmax()\n",
    "#     print(p)\n",
    "    if p == 0:\n",
    "        if index[0] == 0:\n",
    "            print('[]')\n",
    "            return []\n",
    "    points = []\n",
    "#     搜索从该点开始的同一类的点（距离小于dist）\n",
    "    def searchPoint(data,p):\n",
    "        ps = np.argwhere(data[p] < dist)\n",
    "        ps = ps[:,0].reshape(len(ps))\n",
    "        if(len(ps) == 0):\n",
    "            return \n",
    "#         print(ps)\n",
    "#        将搜索到的点保存起来，最后返回出去\n",
    "        points.extend(ps.tolist())\n",
    "    \n",
    "#         plt.scatter(X[:,0],X[:,1],c='b',s=1)\n",
    "#         plt.scatter(X[points,0],X[points,1],c=c)\n",
    "#         plt.xlim(-8,10)\n",
    "#         plt.ylim(1,10)\n",
    "#         plt.show()\n",
    "        data[:,ps] = MAX_DIST\n",
    "#     递归的搜索附近的点\n",
    "        for i in ps:\n",
    "            searchPoint(data,i)\n",
    "        data[ps] = MAX_DIST\n",
    "#     开始搜索\n",
    "    searchPoint(d,p)\n",
    "#     返回找出的点\n",
    "    points = np.array(points).flatten()\n",
    "#     如果没有和这个点很近的点，则返回该点作为单独一类\n",
    "    if len(points) == 0:\n",
    "        points = [p]\n",
    "    return points\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面对数据进行聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_flud_classify(X,y,max_dist):\n",
    "    dist = np.ones((len(X),len(X)),dtype='float64')*MAX_DIST\n",
    "    for i in range(len(X)):\n",
    "        for j in range(i+1,len(X)):\n",
    "            dist[i][j] = (X[i][0] - X[j][0])**2 + (X[i][1] - X[j][1])**2 \n",
    "            dist[j,i] = dist[i,j]\n",
    "    dist = np.sqrt(dist)\n",
    "    data = dist.copy()\n",
    "#      数据的索引，1表示还没有搜索过，0表示已经搜索过了\n",
    "    index = np.ones_like(data[0])\n",
    "    colors = ['r','g','gold','linen','b','lightcoral','magenta','navy','pink','rosybrown','sienna']\n",
    "    i = 0\n",
    "    while (index == 1).any():\n",
    "        points = searchClass(data,max_dist,colors[i % 11],index)\n",
    "#         print(points)\n",
    "        if len(points)==0:\n",
    "            print('='*50)\n",
    "            break\n",
    "        plt.scatter(X[points,0],X[points,1])\n",
    "#         data[points] = MAX_DIST\n",
    "        data[:,points] = MAX_DIST\n",
    "        index[points] = 0\n",
    "        i += 1\n",
    "    plt.title(str(max_dist))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_shuffle,_,y_shuffle,_ = train_test_split(X,y,train_size =0.9,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6NwC7LXoL8aALwF9mHyV2mfiht+/D8PDYb7rDw6dw6O37Yp2lmwjoX4ZDuaUQsk00Bi1xeCnXZLum9Zr13vRrLppTh3u3HsSaJ15h10uEFdODLuLCLnADwIED3xkN3lYmnhnMLX398X5IdqCALiJnAPgMgP9hZjjBZcvCnVq/SkSwaV9H1sDptSPF6V7Wa5b07IldL/HCB12YY5VQMgN3SUmlbSYOlAIYGnedyop4PyQ7UA1dVT9U1bNVNf+Ps3YpWxZ+y5LZqCovHffakGrOWrrXjpRblsxGeYmMO15eKo4lFXa9ENlzKqEMDr7v8I4hlJSMnYsqKakazerjKnYrRbNNNFoTRKUyPtDmCpxeyzXL5zfi3v98MWqrPtqp7awzynHvFy92zLadMnqn40RJ4bVUUlkxDXPm/C0qK6YBkNGf41w/B2K4l0uuicbl8xux5olXbN+bre7tZ6We18moUhEM6fiJVLsPIKIkqaxoGOlYGaustBbD2j8me7cy8Yb6ZbEP4Jlil6G7adPy0y5oV64x3ZFiF8yzHSdKipmzbrYtoZw/+45EZuJOYpehA7kzYz/tgnYdKaY7UBodvgU0si+dEs4K0E4LhZIawDPFMqBbnNoM/QbnfPfzLppTh0df/j3S83H2pROlJLGE4lVsA7pdC+CaJ17B6ideQeNIAN+59qoCj/Ijm/Z14Kk9HWOCuQD4wp/Fe1EIEZkTuxq6xa4F0AqWxbgntNN4tx84VpgBEVHkxDag51r9WWz93dyoi6Kus2szdu5ciG0v/Cl27lwY+31TilFsSy7ZVmpa3Dzt5d6tB9HRfWq0pbAxT8vx/T7AgKgYOK3kBLxPWCZxl0RTYpuhO60KTZctWKbvzAh81DqYr3JNGG2RRPmSbTMsL5K6S6IpsQ3o6f3oQGqCMV1pieBI9ynMWNuGWbf+Et/d9NqY1+1q2pZ8lGuSus0pxYPTSk6vKzxNfTAkVWxLLsD4ja+sNsUzJpSi9/RHwXpIFT95+fcAgO8vvwhA7nJMPnZojPs2p1Q4+S5jOK3k9LoZlqkPhqSKbYaeafn8RuxcexX+456l6Buwf6rMY7veHf17rtp1iQg+vrYNC+55wbH8wieuUzEIo4zhtJJz5qybPU2WOn0AxH2XRFMSE9DTuVlin6sGP6SaM0hz90QqBmGUMRrql9kuwQfg6cMk2wcD5RbrkosTp02wAIzui56+mjS9y8XuvU5PPWIrIhWDsMoYdis5d+5c6OnJQbmW+FN2iQzoX77s3NGaeab0B0rY1bQ/vrbN9n12QZqtiFQMTNW3/fDzYcIl/v4FKrmISK2IbBSRAyLSLiJXmBpYPn1/+UX46uXTbV/LVRLxslMjWxGpGJiqbwPeFw+xJh6uoDX0DQCeVdU5AC4G0B58SOGwulnsZFuQtGhO3bgWSKcgzVZEKgam6tt+JldZEw+X75KLiJwJ4FMA/gsAqOppAKfNDCscAsCuku70QAk/G2ixFZGKgYn6drbJVacSCWvi4QpSQ58J4BiAh0TkYgB7AKxS1d70k0RkJYCVADB9un2ZoxA27euwDeaAcxcMN9CiOPFa3/Y7ucqaeHiClFzKAFwC4IeqOh9AL4C1mSepaquqNqlqU11dXYDbZbdpXwcW3PNCzt5wS7Y6udMDJdi1QnHitb7NenjxCxLQDwM4rKq7Rn7eiFSAD52fBTzZgrDTpKXThGiJCBcLUeR4nSxlPbz4+Q7oqtoF4F0RsaJfM4A3jIzKIz8LeGqqym2Pn3VGuWPN22mx0ZCq5xWgXr9REJnmdbIUAJ/fWeSC9qF/E8CjIjIBwCEANwQfkndeSyGb9nWg9/TguOPlJYLvtVzgeB8r0N/05KuuFxc53T/zaUrp/e+UPG/u6sJLm9/GyeP9mDi5Alcsm4XzL6vP+329TpYuWLCDAbyIBWpbVNVXRurj81R1uaq+b2pgXjiVQpyy8Hu3HsTA0PiJz4mVZa6eKzrsMGnqtpbOLQEo3Zu7urD90QM4ebwfAHDyeD+2P3oAb+7qKsh4uEFWdMViL5dblsxGecn4VsPe04O2pQynwNv94YCr+3lZXGSHk6uU7qXNb2Pw9NgN4wZPD+OlzW8XZDyc/IyuWAT05fMbMbFyfPVoYEhts96gATnoCtCg96d4sTJzt8fzjZOf0RWLgA44Z9d2WW/QgJz58IxSkdGSiZvJTaf7L5pTx4nSBJo4ucLT8Xxzmixl7bz4xWZzrtozyvG+TVC3y3rTd1J08+CJdOkPrKg9oxzlJYKB4bGPp0u/hx27+y+aU4en9nRwojSBrlg2C9sfPTCm7FI2oQRXLJtVsDFxMVA0iTpM8OVDU1OT7t692/h1N+3rwC0/fXU0sFpKJDUx2v3hgKegne0+6d0pThprq7Bz7VWerr3gnhds95Dxcy2KnkJ1uVA0iMgeVW3KdV4sMvR7tx4cF8wBYFgxmrX7zXjTM/KSLPuop/MzucmJ0mQ7/7J6IwG8d99RfLD1HQx196O0tgJnLpmB6vlTDYyQoiAWNXS3Qc9ra2DmClQ3wRxIlX+84kQpBdW77yi6n34LQ92pydSh7n50P/0WevcdLfDIKCyxCOhegl5H9ylP+73kKq/Y8VPF4t7pFNQHW9+BZjwvVweG8cHWdwozIApd5Esum/Z14EObVZ9OW+Ni5LibEozfckfPKXf97OmCTNRSgex/Eth2J9BzGKg5B2i+A5j3pYINx8rM3R6n+Il0QHeapKytKsc1FzeM6Rqxk2u5vtMj5CxOzyb1Wybh3ukhMRGI9z8JbPkWMDDy76Pn3dTPQMGCemlthW3wLq0tTPsjhS/SJRenkkh1RRm+v/yiMb3iTnLtumj/qItU98kPvnQxyyRRYwXinncB6EeBeP+T3q6z7c6Pgrll4FTqeIGcuWQGpHzsf9JSXoIzl8wozIAodJEO6Lk6Q5bPb8TOtVc5BmUgeza9fH4jvnL5dMdHzvERcxGULRDvfxK4/0JgXW3qz2xBvuewt+MhqJ4/FbXXnjeakZfWVqD22vPY5ZIgkS65OJVEMoO003kC573PLd9ffhGa/mSyY22bZZKIcQzE73orodScM5Ll2xwvoOr5UxnAEyzSAf2WJbPH1dDtSh525wmAr1w+3VUwdhO00/vVOaFZxJwCsZQ6Z+52Ab35jrEfAABQXpU6TlQgkQ7objtD8t1Bwv3NI8QpEGcGc4tTRm8F+SLqciEKtPRfRN4BcALAEIDBXEtT87X0v9C4bD9i7Lpctt3pUEI5F1jzm/DHSJQmzKX/i1T1PQPXiSwu2w+Jqb7veV+yf18YJZQi612neIl0yaVYuJ2cpQDy3fcdRgmlCHvXKV6Cti0qgOdEZI+IrDQxoCjisv0QhNH3Pe9LqfLKuu7Un6aDbBH2rlO8BM3QF6jqERGZCuB5ETmgqr9KP2Ek0K8EgOnTpwe8XXHisv0Q+On7NlXeMHWdIuxdp3gJFNBV9cjIn0dF5GcALgXwq4xzWgG0AqlJ0SD3K2bsR88zr33fpsobJsskRdq7TvHhu+QiItUiMsn6O4DFANgOQPnRfEdqkjJdtklLU+UNk2USr78DkUdBMvSPAfiZiFjX+RdVfdbIqCjZspU43JY+TJU3TJZJ2LtOeeY7oKvqIQAXGxwLUe4Sh9vgZ6q8YbpM4uV3IPIo0ptzUUR42fTKVInDVHmDZRKKEPahU355nVQ0VeIwVd5gmYQihAGd8itbxp3vXQxNlTdYJqGIYMmF8strxs0SB5FvDOiUX06ZtdPxeV8CWv4htSkWJPVnyz9kz5C91OiJYowlF8ovP/uGeylxcH8UolHM0Cm/smXcJjJr7o9CNIoZOnnyVNdx3H2oEx39A2isKMetMxvwhfrJ2d9kl3Gbyqy5PwrRKGbo5NpTXcdx88F3cbh/AArgcP8Abj74Lp7qOu79YqYya681eqIYY0An1+4+1IlTw2P3Vzs1rLj7UKf3i5nKrNkVQzSKAZ1c6+gf8HQ8K1OZtZ+uGKKYYg2dXGusKMdhm+DdWFHu/WJ+ul+ccOEPEQBm6OTgqa7jaHrxdTRsfwVNL76Op7qO49aZDagqkTHnVZUIbp3Z4P0GzKyJjBPV8J450dTUpLt37w7tfuSPNfmZXi+vKhHcN/tcAPDe5UJEgYjIHlVtynUeSy40TrbJz91XXsAATlSkGNATwkv/uNHJTyIKTeCALiKlAHYD6FDVa4IPiUzLLKFY/eMAbIO60clPSpz2Hdux4/FHcOKP72HS2VOwcMV1mLtwkZFrtx1qw4a9G9DV24X66nqsumQVls5cauTacWBiUnQVgHYD16E88do/bnTykxKlfcd2PNf6AE68dwxQxYn3juG51gfQvmN74Gu3HWrDuhfXobO3EwpFZ28n1r24Dm2H2gyMPB4CBXQROQfAUgA/MjMcygevJZQv1E/GfbPPxTkV5RAA51SU477Z57J2TjntePwRDJ7uH3Ns8HQ/djz+iO9rth1qw+KNi7F2x1r0DfWNea1vqA8b9m7wfe24CVpyWQ/g2wAmOZ0gIisBrASA6dOnB7wd+eGnhPKF+smeArivPV4odk788T1Px3OxsvLMQJ6uq7fL17XjyHeGLiLXADiqqnuynaeqrarapKpNdXV1fm9HAeS7hGJ0jxeKtElnT7E9LiL4wYoWtN54g6fyy4a9G7IGcwCor673NMY4C1JyWQDg8yLyDoDHAVwlIj8xMioyKt8lFKN7vFCkLVxxHcomVIw7rsPDvmrqubLvytJKrLpkla+xxpHvkouq3grgVgAQkb8AcLOqftXQuMgwryUUL9jmSBarm8XqchGRVDBPY9XU3XS+1FfXo7PXPjFoqG5gl0sGLv1POLsl/l451eLZ5phMcxcuwsoHH8JNj2+B00p0tzX1VZesQmVp5ZhjlaWV+MvZfwkAuHXHrVi8cTE7XUYYCeiq+m/sQY8eU7VvtjmSE6eautPxTEtnLsW6K9ehoboBAkFDdQOW/ekybP7tZrYv2uBeLgnW9OLrtt0v51SUY/eVF3i6Frtc4sPkwiCrLz29lbFsQgUWr/yG72su3rjYtgzTUN2A5774nK9rFjvu5UI5max957NGT+HJDMDWJCYAXwE4s6ZuYuWo00Qp2xcZ0Itez5YtOHr/egx2dqKsoQFT16xGTUuLkWtziT9lyrYwyG8QnrtwkbGl/4DzRCnbFzkpWtR6tmxB5+13YPDIEUAVg0eOoPP2O9CzZYuR67P2TZlMLwzKB6eJUrYvMqAXtaP3r4f2jV1UoX19OHr/eiPX5xJ/yhR0EjMMdhOl665cx/ZFsORS1AY77ftvnY77wdo3pVu44jrbScyFK67L+r70idSK6okQAfpOnnSsmQedeF06cykDuA1m6AH0bNmCt65qRvvcT+Ctq5qNlUIsZQ32pQ+n42GMieJt7sJFWLzyG5g0pQ4QwaQpdTk7UjJ3WOw/eQJ9J044rgzN546MSccM3Servm2VRKz6NgBjk5ZT16wecw8AkMpKTF2zumBjovjzOolpN5GaLnNSNR8Tr5TCDN2nfNe3gVQQbrjrTpRNmwaIoGzaNDTcdadjcA5jTESZ3EyYpp8ThYnXqGKG7lO2+rbJVsOalhbX7w2j5k6UadLZU1Llkxzn5Dq/mCZeo4oZuk9OdezSmpq8thr6GVO2mjtRUE47LFoyJ1Xtzncz8epF+47taL3xBl9b9kYZA7pPU9eshlSO7YWVykoMA77KHiYmM53G5FRzJzIhcyK1YuIkVE6a5Dip6mfi1YskT7pyL5cA7EorR779vwC7/5uKYG77G47XsZv8zFYv9zImTohSkrTeeIN9SWdKHVY++FABRhQc93IJgV19++j961PllgzZyh7ZJjO9BmMvNXeiYhS0R91x0jVHnT8OWHIxLFfZw660wslMohQT5ZJsk6txL7swoBuWrdXQaW+W0poa22txMpOiwtQkZLYedbeyTa56uU4U+S65iEglgF8BqBi5zkZV/Z6pgUWZU9nDqbQyXFkJqax0vYCIqJiY2HJ3tMziUBbx0qM+d+Ei/PKBHwS+ThQFydD7AVylqhcD+CSAq0XkcjPDiienEor29HhaQERUTJyy6m0/bnX1/jFlFgdeetTbd2yHlNiHtrgl6wmpAAAJJUlEQVT3ugd5SLQCODnyY/nI//LaMtO77yg+2PoOhrr7UVpbgTOXzED1/Kn5vKVRZQ0NjhOmnMykQvM7GemU9fafPIH2HdtzXiPX1gFeetTbd2zHMz9cP+7B1F6vE1WBaugiUioirwA4CuB5Vd1lc85KEdktIruPHfM/y9y77yi6n34LQ92p/8cPdfej++m30LvvqO9rho194lSsgkxGZst63dSss5VBvPaob/txK3RoyPY1k73uxSpQQFfVIVX9JIBzAFwqIhfanNOqqk2q2lRXV+f7Xh9sfQc6MPZTVweG8cHWd3xfM2xe92YhCkuQychsWa+bmrXjHuwjfeNegnD/yROOr8U9mAOGulxUtRvAvwG42sT17FiZudvjxaqmpQXnvbANc9vfwHkvbGMwp6IQZMOsuQsXpVaG2nBTsza1FUDcWxLd8B3QRaRORGpH/l4F4NMADpgaWKbSWvu9IpyOE5F7QZ9UdNX1K30HZVNbAWT7NuH0gRM3QVaKNgB4WERKkfpgeFJVf2FmWOOduWQGup9+a0zZRcpLcOaSGfm6JVFi+H1SkSV9r3M/KzxNPEg627eJq65fGejaURGky2U/gPkGx5KV1c3ipcvFVFdM1LtriHIJGpCLgdO2vBUTJ0Xq9wgitptzWV0xmRl97bXneQrGpq5DFGeZi4uAVIbvpXTitm3S6TwTYyhWidqcyy6DztYV4yUQm7oOUZwFfayc29Wmbs6L8reMoCIf0DMzaKs/PTMIW7x2xcSlu4Yon4I+Vs7tB0Ku80zU4qMs8gHdKYOGwHbdarauGLtMv7S2wjZ4s7uG6CNBHyvn9gOBzyPNLvIB3TFT1lSt221XjFOmX/VnU3Fqz1F211CipdetKydOhCrQ33tytKwRtEvG7QcCn0eaXeS3z83Wn1577Xmjr1s/V8+fit59R9F5z7/j8Nod6Lzn30czc7tMv//A+47XIUqCzG0B+k6cSK3ITNsiAECgXnK3i4vCeB5plEU+Q8/Wn149f+q4wOun5m53HaKkyLV5llXDdrtMP1s3S64JTevnFx5uRd+J1DL/0gkTXP0eQZ+EFAWRD+he+9NN1tyJksBNffrEe8fQeuMNOYNkri4VtwF2sP/06N/7T57Iuf+6iT3boyDyAR2ApwzaVM2dKCmc6taZ3ATJoO2Nfq9h4r5REPkauld+au5ESWZXt3aSa4dGE10qfq6RlO6YWGToXnituRMlXWZ9u3LixNH6tZ2s+5sb6FJxXOJfPTGv942CxGXo1fOnMhMn8mjuwkVY+eBDuOnxLbjxR48BIo7nVk50DqwmulQWrrgOUlo67vhA3ynHLXST0h2TuAwd8FZzJ6LxstXV+z/80PHRcyaW589duGhMl4tleHDQsSaelG0BEhnQiWgsry19dguJLDo0lHWy0a6bxev9+06etD2erdyThG0BGNCJEs7LxljpQfeCP2/Gq8//0vaaXiYb/bQUJqUm7lWQJxadKyLbRaRdRF4XkVUmB0ZE4XDzPNH2HdvxzA/Xj3mI9P4XtqJiov9Hz3m5f6ak1MS9CpKhDwK4SVX3isgkAHtE5HlVfcPQ2IgoBG5a+rb9uBU6NDTmdR0awtDAaZRNqPC9h4vb+2dKSk3cqyBPLOoE0Dny9xMi0g6gEQADOlGEuClf9J+0b1Mc7O/H575xU6DA6rd8koSauFdGaugiMgOpx9HtMnE9IgqPieeJBgmsQe9PHwkc0EVkIoCnAKxW1Q9sXl8JYCUATJ8+PejtiMgwN+WLykmTbBcTVU6yr6Gbvj+5E+iZoiJSDuAXALaq6t/nOj/MZ4oSkTntO7bj2f+zAcODg6PHSsrKcPXXVzHwhiDvzxQVEQHwjwDa3QRzIoouZtHREKTksgDA1wC8JiKvjBy7TVXtG1OJKNI4CVn8gnS5/BqpXcSJiKgIJG5zLiKiuGJAJyKKCQZ0IqKYYEAnIooJBnQiophgQCciigkGdCKimAi09N/zzUSOAfidgUtNARCvx3Xnxt85Gfg7J4PX3/lPVLUu10mhBnRTRGS3m30N4oS/czLwd06GfP3OLLkQEcUEAzoRUUxENaC3FnoABcDfORn4OydDXn7nSNbQiYhovKhm6ERElIEBnYgoJiId0EXkZhFREcn+ePAYEJF7ReSAiOwXkZ+JSG2hx5QvInK1iBwUkd+KyNpCjyffRORcEdkuIu0i8rqIrCr0mMIiIqUisk9EflHosYRBRGpFZOPIf8vtInKFyetHNqCLyLkAPgPg94UeS0ieB3Chqs4D8CaAWws8nrwQkVIADwL4LIBPAPiyiHyisKPKu0EAN6nqXACXA7gxAb+zZRWA9kIPIkQbADyrqnMAXAzDv3tkAzqA+wF8G0AiZnVV9TlVtZ7Q+zKAcwo5njy6FMBvVfWQqp4G8DiAZQUeU16paqeq7h35+wmk/iNvLOyo8k9EzgGwFMCPCj2WMIjImQA+hdSzmKGqp1W12+Q9IhnQReTzADpU9dVCj6VA/iuAZwo9iDxpBPBu2s+HkYDgZhGRGQDmA9hV2JGEYj1SSdlwoQcSkpkAjgF4aKTM9CMRqTZ5gyAPic4rEflXAPU2L30HwG0AFoc7ovzL9jur6uaRc76D1Ff0R8McW4jsnlObiG9hIjIRwFMAVqvqB4UeTz6JyDUAjqrqHhH5i0KPJyRlAC4B8E1V3SUiGwCsBXC7yRsUJVX9tN1xEbkIwMcBvCoiQKr0sFdELlXVrhCHaJzT72wRkesBXAOgWeO7gOAwgHPTfj4HwJECjSU0IlKOVDB/VFWfLvR4QrAAwOdF5HMAKgGcKSI/UdWvFnhc+XQYwGFVtb59bUQqoBsT+YVFIvIOgCZVjfVubSJyNYC/B/Dnqnqs0OPJFxEpQ2rStxlAB4D/B+CvVPX1gg4sjySVmTwM4Liqri70eMI2kqHfrKrXFHos+SYiOwD8taoeFJF1AKpV9RZT1y/aDJ3GeQBABYDnR76ZvKyqXy/skMxT1UER+QaArQBKAfxTnIP5iAUAvgbgNRF5ZeTYbar6ywKOifLjmwAeFZEJAA4BuMHkxSOfoRMRUUoku1yIiGg8BnQiophgQCciigkGdCKimGBAJyKKCQZ0IqKYYEAnIoqJ/w8xpIDGODqQkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for md in [0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2]:\n",
    "    fill_flud_classify(X_shuffle,y_shuffle,md)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "距离取0.8和0.9 都可以以分开"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
